System and method for an integrated, multi-modal, multi-device natural language voice services environment

ABSTRACT

A system and method for an integrated, multi-modal, multi-device natural language voice services environment may be provided. In particular, the environment may include a plurality of voice-enabled devices each having intent determination capabilities for processing multi-modal natural language inputs in addition to knowledge of the intent determination capabilities of other devices in the environment. Further, the environment may be arranged in a centralized manner, a distributed peer-to-peer manner, or various combinations thereof. As such, the various devices may cooperate to determine intent of multi-modal natural language inputs, and commands, queries, or other requests may be routed to one or more of the devices best suited to take action in response thereto.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/127,343, entitled “System and Method for an Integrated, Multi-Modal,Multi-Device Natural Language Voice Services Environment,” filed May 27,2008 (which issues as U.S. Pat. No. 8,589,161 on Nov. 19, 2013), thecontent of which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The invention relates to an integrated voice services environment inwhich a plurality of devices can provide various voice services bycooperatively processing free form, multi-modal, natural languageinputs, thereby facilitating conversational interactions between a userand one or more of the devices in the integrated environment.

BACKGROUND OF THE INVENTION

As technology has progressed in recent years, consumer electronicdevices have emerged to become nearly ubiquitous in the everyday livesof many people. To meet the increasing demand that has resulted fromgrowth in the functionality and mobility of mobile phones, navigationdevices, embedded devices, and other such devices, a wealth of featuresand functions are often provided therein in addition to coreapplications. Greater functionality also introduces the trade-offs,however, including learning curves that often inhibit users from fullyexploiting all of the capabilities of their electronic devices. Forexample, many existing electronic devices include complex human tomachine interfaces that may not be particularly user-friendly, whichinhibits mass-market adoption for many technologies. Moreover,cumbersome interfaces often result in otherwise desirable features beingburied (e.g., within menus that may be tedious to navigate), which hasthe tendency of causing many users to not use, or even know about, thepotential capabilities of their devices.

As such, the increased functionality provided by many electronic devicesoften tends to be wasted, as market research suggests that many usersonly use only a fraction of the features or applications available on agiven device. Moreover, in a society where wireless networking andbroadband access are increasingly prevalent, consumers tend to naturallydesire seamless mobile capabilities from their electronic devices. Thus,as consumer demand intensifies for simpler mechanisms to interact withelectronic devices, cumbersome interfaces that prevent quick and focusedinteraction can become an important concern. Accordingly, theever-growing demand for mechanisms to use technology in intuitive waysremains largely unfulfilled.

One approach towards simplifying human to machine interactions inelectronic devices includes the use of voice recognition software, whichcan enable users to exploit features that could otherwise be unfamiliar,unknown, or difficult to use. For example, a recent survey conducted bythe Navteq Corporation, which provides data used in a variety ofapplications such as automotive navigation and web-based applications,demonstrates that voice recognition often ranks among the features mostdesired by consumers of electronic devices. Even so, existing voice userinterfaces, when they actually work, still tend to require significantlearning on the part of the user.

For example, many existing voice user interface only support requestsformulated according to specific command-and-control sequences orsyntaxes. Furthermore, many existing voice user interfaces cause userfrustration or dissatisfaction because of inaccurate speech recognition.Similarly, by forcing a user to provide pre-established commands orkeywords to communicate requests in ways that a system can understand,existing voice user interfaces do not effectively engage the user in aproductive, cooperative dialogue to resolve requests and advance aconversation towards a mutually satisfactory goal (e.g., when users maybe uncertain of particular needs, available information, or devicecapabilities, among other things). As such, existing voice userinterfaces tend to suffer from various drawbacks, including significantlimitations on engaging users in a dialogue in a cooperative andconversational manner.

Additionally, many existing voice user interfaces fall short inutilizing information distributed across various different domains ordevices in order to resolve natural language voice-based inputs. Thus,existing voice user interfaces suffer from being constrained to a finiteset of applications for which they have been designed, or to devices onwhich they reside. Although technological advancement has resulted inusers often having several devices to suit their various needs, existingvoice user interfaces do not adequately free users from deviceconstraints. For example, users may be interested in services associatedwith different applications and devices, but existing voice userinterfaces tend to restrict users from accessing the applications anddevices as they see fit. Moreover, users typically can only practicablycarry a finite number of devices at any given time, yet content orservices associated with users' other devices that currently being usedmay be desired in various circumstances. Accordingly, although userstend to have varying needs, where content or services associated withdifferent devices may be desired in various contexts or environments,existing voice technologies tend to fall short in providing anintegrated environment in which users can request content or servicesassociated with virtually any device or network. As such, constraints oninformation availability and device interaction mechanisms in existingvoice services environments tend to prevent users from experiencingtechnology in an intuitive, natural, and efficient way.

Existing systems suffer from these and other problems.

SUMMARY OF THE INVENTION

According to various aspects of the invention, a system and method foran integrated, multi-modal, multi-device natural language voice servicesenvironment may include a plurality of voice-enabled devices each havingintent determination capabilities for processing multi-modal naturallanguage inputs in addition to knowledge of the intent determinationcapabilities of other devices in the environment. Further, theenvironment may be arranged in a centralized manner, a distributedpeer-to-peer manner, or various combinations thereof. As such, thevarious devices may cooperate to determine intent of multi-modal naturallanguage inputs, and commands, queries, or other requests may be routedto one or more of the devices best suited to take action in responsethereto.

According to various aspects of the invention, the integrated naturallanguage voice services environment arranged in the centralized mannerincludes an input device that receives a multi-modal natural languageinput, a central device communicatively coupled to the input device, andone or more secondary devices communicatively coupled to the centraldevice. Each of the input device, the central device, and the one ormore secondary devices may have intent determination capabilities forprocessing multi-modal natural language inputs. As such, an intent of agiven multi-modal natural language input may be determined in thecentralized manner by communicating the multi-modal natural languageinput from the input device to the central device. Thereafter, thecentral device may aggregate the intent determination capabilities ofthe input device and the one or more secondary devices and determine anintent of the multi-modal natural language input using the aggregatedintent determination capabilities. The input device may then receive thedetermined intent from the central device and invoke at least one actionat one or more of the input device, the central device, or the secondarydevices based on the determined intent.

According to various aspects of the invention, the integrated naturallanguage voice services environment arranged in the distributed mannerincludes an input device that receives a multi-modal natural languageinput, a central device communicatively coupled to the input device andone or more secondary devices communicatively coupled to the inputdevice, wherein each of the input device and the one or more secondarydevices may have intent determination capabilities for processingmulti-modal natural language inputs, as in the centralizedimplementation. However, the distributed implementation may be distinctfrom the centralized implementation in that a preliminary intent of themulti-modal natural language input may be determined at the input deviceusing local intent determination capabilities. The multi-modal naturallanguage input may then be communicated to one or more of the secondarydevices (e.g., when a confidence level of the intent determination atthe input device falls below a given threshold). In such cases, each ofthe secondary devices determine an intent of the multi-modal naturallanguage input using local intent determination capabilities. The inputdevice collates the preliminary intent determination and the intentdeterminations of the secondary devices, and may arbitrate among thecollated intent determinations to determine an actionable intent of themulti-modal natural input.

According to various aspects of the invention, the integrated naturallanguage voice services environment arranged in a manner thatdynamically selects between a centralized model and a distributed model.For example, the environment includes an input device that receives amulti-modal natural language input one or more secondary devicescommunicatively coupled to the input device, each of which have intentdetermination capabilities for processing multi-modal natural languageinputs. A constellation model may be accessible to each of the inputdevice and the one or more secondary devices, wherein the constellationmodel describes the intent determination capabilities of the inputdevice and the one or more secondary devices. The multi-modal naturallanguage input can be routed for processing at one or more of the inputdevice or the secondary devices to determine an intent thereof based onthe intent determination capabilities described in the constellationmodel. For example, when the constellation model arranges the inputdevice and the secondary devices in the centralized manner, one of thesecondary devices may be designated the central device and the naturallanguage input may be processed as described above. However, when themulti-modal natural language cannot be communicated to the centraldevice, the constellation model may be dynamically rearranged in thedistributed manner, whereby the input device and the secondary devicesshare knowledge relating to respective local intent determinationcapabilities and operate as cooperative nodes to determine the intent ofthe multi-modal natural language input using the shared knowledgerelating to local intent determination capabilities.

Other objects and advantages of the invention will be apparent based onthe following drawings and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an exemplary multi-modalelectronic device that may be provided in an integrated, multi-devicenatural language voice services environment, according to variousaspects of the invention.

FIG. 2 illustrates a block diagram of an exemplary centralizedimplementation of the integrated, multi-modal, multi-device naturallanguage voice service environment, according to various aspects of theinvention.

FIG. 3 illustrates a flow diagram of an exemplary method for processingmulti-modal, natural language inputs at an input device in thecentralized implementation of the integrated, multi-modal, multi-devicenatural language voice service environment, according to various aspectsof the invention.

FIG. 4 illustrates a flow diagram of an exemplary method for processingmulti-modal, natural language inputs at a central device in thecentralized implementation of the integrated, multi-modal, multi-devicenatural language voice service environment, according to various aspectsof the invention.

FIG. 5 illustrates a flow diagram of an exemplary method for processingmulti-modal, natural language inputs at a secondary device in thecentralized implementation of the integrated, multi-modal, multi-devicenatural language voice service environment, according to various aspectsof the invention.

FIG. 6 illustrates a block diagram of an exemplary distributedimplementation of the integrated, multi-modal, multi-device naturallanguage voice service environment, according to various aspects of theinvention.

FIG. 7 illustrates a flow diagram of an exemplary method for processingmulti-modal, natural language inputs at an input device in thedistributed implementation of the integrated, multi-modal, multi-devicenatural language voice service environment, according to various aspectsof the invention.

DETAILED DESCRIPTION

According to various aspects of the invention, FIG. 1 illustrates ablock diagram of an exemplary multi-modal electronic device 100 that maybe provided in a natural language voice services environment thatincludes one or more additional multi-modal devices (e.g., asillustrated in FIGS. 2 and 6). As will be apparent, the electronicdevice 100 illustrated in FIG. 1 may be any suitable voice-enabledelectronic device (e.g., a telematics device, a personal navigationdevice, a mobile phone, a VoIP node, a personal computer, a mediadevice, an embedded device, a server, or another electronic device). Thedevice 100 may include various components that collectively provide acapability to process conversational, multi-modal natural languageinputs. As such, a user of the device 100 may engage in multi-modalconversational dialogues with the voice-enabled electronic device 100 toresolve requests in a free form, cooperative manner.

For example, the natural language processing components may support freeform natural language utterances to liberate the user from restrictionsrelating to how commands, queries, or other requests should beformulated. Rather, the user may employ any manner of speaking thatfeels natural in order to request content or services available throughthe device 100 (e.g., content or services relating to telematics,communications, media, messaging, navigation, marketing, informationretrieval, etc.). For instance, in various implementations, the device100 may process natural language utterances utilizing techniquesdescribed in co-pending U.S. patent application Ser. No. 10/452,147,entitled “Systems and Methods for Responding to Natural Language SpeechUtterance,” filed Jun. 3, 2003, and U.S. patent application Ser. No.10/618,633, entitled “Mobile Systems and Methods for Responding toNatural Language Speech Utterance,” filed Jun. 15, 2003, the disclosuresof which are hereby incorporated by reference in their entirety.

Moreover, because the device 100 may be deployed in an integratedmulti-device environment, the user may further request content orservices available through other devices deployed in the environment. Inparticular, the integrated voice services environment may include aplurality of multi-modal devices, each of which include natural languagecomponents generally similar to those illustrated in FIG. 1. The variousdevices in the environment may serve distinct purposes, however, suchthat available content, services, applications, or other capabilitiesmay vary among the devices in the environment (e.g., core functions of amedia device may vary from those of a personal navigation device). Thus,each device in the environment, including device 100, may have knowledgeof content, services, applications, intent determination capabilities,and other features available through the other devices by way of aconstellation model 130 b. Accordingly, as will be described in greaterdetail below, the electronic device 100 may cooperate with other devicesin the integrated environment to resolve requests by sharing context,prior information, domain knowledge, short-term knowledge, long-termknowledge, and cognitive models, among other things.

According to various aspects of the invention, the electronic device 100may include an input mechanism 105 that can receive multi-modal naturallanguage inputs, which include at least an utterance spoken by the user.As will be apparent, the input mechanism 105 may include any appropriatedevice or combination of devices capable of receiving a spoken input(e.g., a directional microphone, an array of microphones, or any otherdevice that can generate encoded speech). Further, in variousimplementations, the input mechanism 105 can be configured to maximizefidelity of encoded speech, for example, by maximizing gain in adirection of the user, cancelling echoes, nulling point noise sources,performing variable rate sampling, or filtering environmental noise(e.g., background conversations). As such, the input mechanism 105 maygenerate encoded speech in a manner that can tolerate noise or otherfactors that could otherwise interfere with accurate interpretation ofthe utterance.

Furthermore, in various implementations, the input mechanism 105 mayinclude various other input modalities (i.e., the input mechanism 105may be arranged in a multi-modal environment), in that non-voice inputscan be correlated and/or processed in connection with one or moreprevious, contemporaneous, or subsequent multi-modal natural languageinputs. For example, the input mechanism 105 may be coupled to atouch-screen interface, a stylus and tablet interface, a keypad orkeyboard, or any other suitable input mechanism, as will be apparent. Asa result, an amount of information potentially available when processingthe multi-modal inputs may be maximized, as the user can clarifyutterances or otherwise provide additional information in a givenmulti-modal natural language input using various input modalities. Forinstance, in an exemplary illustration, the user could touch a stylus orother pointing device to a portion of a touch-screen interface of thedevice 100, while also providing an utterance relating to the touchedportion of the interface (e.g., “Show me restaurants around here”). Inthis example, the natural language utterance may be correlated with theinput received via the touch-screen interface, resulting in “aroundhere” being interpreted in relation to the touched portion of theinterface (e.g., as opposed to the user's current location or some othermeaning).

According to various aspects of the invention, the device 100 mayinclude an Automatic Speech Recognizer 110 that generates one or morepreliminary interpretations of the encoded speech, which may be receivedfrom the input mechanism 105. For example, the Automatic SpeechRecognizer 110 may recognize syllables, words, or phrases contained inan utterance using one or more dynamically adaptable recognitiongrammars. The dynamic recognition grammars may be used to recognize astream of phonemes through phonetic dictation based on one or moreacoustic models. Furthermore, as described in co-pending U.S. patentapplication Ser. No. 11/197,504, entitled “Systems and Methods forResponding to Natural Language Speech Utterance,” filed Aug. 5, 2005,the disclosure of which is hereby incorporated by reference in itsentirety, the Automatic Speech Recognizer 110 may be capable ofmulti-pass analysis, where a primary speech recognition engine maygenerate a primary interpretation of an utterance (e.g., using a largelist dictation grammar) and request secondary transcription from one ormore secondary speech recognition engines (e.g., using a virtualdictation grammar having decoy words for out-of-vocabulary words).

Thus, the Automatic Speech Recognizer 110 may generate preliminaryinterpretations of an utterance in various ways, including exclusive useof a dictation grammar or virtual dictation grammar, or use of variouscombinations thereof (e.g., when the device 100 supports multi-passanalysis). In any event, the Automatic Speech Recognizer 110 may provideout-of-vocabulary capabilities and may tolerate portions of a speechsignal being dropped, the user misspeaking, or other variables that mayoccur in natural language speech (e.g., stops and starts, stutters,etc.). Furthermore, the recognition grammars employed by the AutomaticSpeech Recognizer 110 may include vocabularies, dictionaries, syllables,words, phrases, or other information optimized according to variouscontextual or application-specific domains (e.g., navigation, music,movies, weather, shopping, news, languages, temporal or geographicproximities, or other suitable domains). Moreover, environmentalknowledge (e.g., peer-to-peer affinities, capabilities of devices in theenvironment, etc.), historical knowledge (e.g., frequent requests, priorcontext, etc.), or other types of knowledge can be used to continuallyoptimize the information contained in the recognition grammars on adynamic basis.

For example, information contained in the recognition grammars may bedynamically optimized to improve a likelihood of a given utterance beingrecognized accurately (e.g., following an incorrect interpretation of aword, the incorrect interpretation may be removed from the grammar toreduce a likelihood of the incorrect interpretation being repeated).Accordingly, the Automatic Speech Recognizer 110 may use a number oftechniques to generate preliminary interpretations of natural languageutterances, including those described, for example, in co-pending U.S.patent application Ser. No. 11/513,269, entitled “Dynamic SpeechSharpening,” filed Aug. 31, 2006, the disclosure of which is herebyincorporated by reference in its entirety. Furthermore, the techniquesused by the Automatic Speech Recognizer 110 associated with the device100 may be considered in defining intent determination capabilities ofthe device 100, and such capabilities may be shared with other devicesin the environment to enable convergence of speech recognitionthroughout the environment (e.g., because various devices may employdistinct speech recognition techniques or have distinct grammars orvocabularies, the devices may share vocabulary translation mechanisms toenhance system-wide recognition).

According to various aspects of the invention, the Automatic SpeechRecognizer 110 may provide one or more preliminary interpretations of amulti-modal input, including an utterance contained therein, to aconversational language processor 120. The conversational languageprocessor 120 may include various components that collectively operateto model everyday human-to-human conversations in order to engage incooperative conversations with the user to resolve requests based on theuser's intent. For example, the conversational language processor 120may include, among other things, an intent determination engine 130 a, aconstellation model 130 b, one or more domain agents 130 c, a contexttracking engine 130 d, a misrecognition engine 130 e, and a voice searchengine 130 f. Furthermore, the conversational language processor 120 maybe coupled to one or more data repositories 160 and applicationsassociated with one or more domains. Thus, the intent determinationcapabilities of the device 100 may be defined based on the data andprocessing capabilities of the Automatic Speech Recognizer 110 and theconversational language processor 120.

More particularly, the intent determination engine 130 a may establishmeaning for a given multi-modal natural language input based on aconsideration of the intent determination capabilities of the device 100as well as the intent determination capabilities of other devices in theintegrated voice services environment. For example, the intentdetermination capabilities of the device 100 may be defined as afunction of processing resources, storage for grammars, context, agents,or other data, and content or services associated with the device 100(e.g., a media device with a small amount of memory may have a smallerlist of recognizable songs than a device with a large amount of memory).Thus, the intent determination engine 130 a may determine whether toprocess a given input locally (e.g., when the device 100 has intentdetermination capabilities that suggest favorable conditions forrecognition), or whether to route information associated with the inputto other devices, which may assist in determining the intent of theinput.

As such, to determine which device or combination of devices shouldprocess an input, the intent determination engine 130 a may evaluate theconstellation model 130 b, which provides a model of the intentdetermination capabilities for each of the devices in the integratedvoice services environment. For instance, the constellation model 130 bmay contain, among other things, knowledge of processing and storageresources available to each of the devices in the environment, as wellas the nature and scope of domain agents, context, content, services,and other information available to each of the devices in theenvironment. As such, using the constellation model 130 b, the intentdetermination engine 130 a may be able to determine whether any of theother devices have intent determination capabilities that can be invokedto augment or otherwise enhance the intent determination capabilities ofthe device 100 (e.g., by routing information associated with amulti-modal natural language input to the device or devices that appearbest suited to analyze the information and therefore determine an intentof the input). Accordingly, the intent determination engine 130 a mayestablish the meaning of a given utterance by utilizing thecomprehensive constellation model 130 b that describes capabilitieswithin the device 100 and across the integrated environment. The intentdetermination engine 130 a may therefore optimize processing of a givennatural language input based on capabilities throughout the environment(e.g., utterances may be processed locally to the device 100, routed toa specific device based on information in the constellation model 130 b,or flooded to all of the devices in the environment in which case anarbitration may occur to select a best guess at an intentdetermination).

Although the following discussion will generally focus on varioustechniques that can be used to determine the intent of multi-modalnatural language inputs in the integrated multi-device environment, itwill be apparent that the natural language processing capabilities ofany one of the devices may extend beyond the specific discussion thathas been provided herein. As such, in addition to the co-pending U.S.patent applications referenced above, further natural languageprocessing capabilities that may be employed include those described inco-pending U.S. patent application Ser. No. 11/197,504, entitled“Systems and Methods for Responding to Natural Language SpeechUtterance,” filed Aug. 5, 2005, U.S. patent application Ser. No.11/200,164, entitled “System and Method of Supporting AdaptiveMisrecognition in Conversational Speech,” filed Aug. 10, 2005, U.S.patent application Ser. No. 11/212,693, entitled “Mobile Systems andMethods of Supporting Natural Language Human-Machine Interactions,”filed Aug. 29, 2005, U.S. patent application Ser. No. 11/580,926,entitled “System and Method for a Cooperative Conversational Voice UserInterface,” filed Oct. 16, 2006, U.S. patent application Ser. No.11/671,526, entitled “System and Method for Selecting and PresentingAdvertisements Based on Natural Language Processing of Voice-BasedInput,” filed Feb. 6, 2007, and U.S. patent application Ser. No.11/954,064, entitled “System and Method for Providing a Natural LanguageVoice User Interface in an Integrated Voice Navigation ServicesEnvironment,” filed Dec. 11, 2007, the disclosures of which are herebyincorporated by reference in their entirety.

According to various aspects of the invention, FIG. 2 illustrates ablock diagram of an exemplary centralized implementation of theintegrated, multi-modal, multi-device natural language voice serviceenvironment. As will be apparent from the further description to beprovided herein, the centralized implementation of the integrated,multi-device voice services environment may enable a user to engage inconversational, multi-modal natural language interactions with any oneof voice-enabled devices 210 a-n or central voice-enabled device 220. Assuch, the multi-device voice services environment may collectivelydetermine intent for any given multi-modal natural language input,whereby the user may request content or voice services relating to anydevice or application in the environment, without restraint.

As illustrated in FIG. 2, the centralized implementation of themulti-device voice service environment may include a plurality ofvoice-enabled devices 210 a-n, each of which include various componentscapable of determining intent of natural language utterances, asdescribed above in reference to FIG. 1. Furthermore, as will beapparent, the centralized implementation includes a central device 220,which contains information relating to intent determination capabilitiesfor each of the other voice-enabled devices 210 a-n. For example, invarious exemplary implementations, the central device 220 may bedesignated as such by virtue of being a device most capable ofdetermining the intent of an utterance (e.g., a server, home datacenter, or other device having significant processing power, memoryresources, and communication capabilities making the device suitable tomanage intent determination across the environment). In anotherexemplary implementation, the central device 220 may be dynamicallyselected based on one or more characteristics of a given multi-modalnatural language input, dialogue, or interaction (e.g., a device may bedesignated as the central device 220 when a current utterance relates toa specific domain).

In the centralized implementation illustrated in FIG. 2, a multi-modalnatural language input may be received at one of the voice-enableddevices 210 a-n. Therefore, the receiving one of the devices 210 a-n maybe designated as an input device for that input, while the remainingdevices 210 a-n may be designated as secondary devices for that input.In other words, for any given multi-modal natural language input, themulti-device environment may include an input device that collects theinput, a central device 220 that aggregates intent determination,inferencing, and processing capabilities for all of the devices 210 a-nin the environment, and one or more secondary devices that may also beused in the intent determination process. As such, each device 210 inthe environment may be provided with a constellation model thatidentifies all of the devices 210 having incoming and outgoingcommunication capabilities, thus indicating an extent to which otherdevices may be capable of determining intent for a given multi-modalnatural language input. The constellation model may further define alocation of the central device 220, which aggregates context,vocabularies, content, recognition grammars, misrecognitions, sharedknowledge, intent determination capabilities, inferencing capabilities,and other information from the various devices 210 a-n in theenvironment.

Accordingly, as communication and processing capabilities permit, thecentral device 220 may be used as a recognizer of first or last resort.For example, because the central device 220 converges intentdetermination capabilities across the environment (e.g., by aggregatingcontext, vocabularies, device capabilities, and other information fromthe devices 210 a-n in the environment), inputs may be automaticallyrouted to the central device 220 when used as a recognizer of firstresort, or as a recognizer of last resort when local processing at theinput device 210 cannot determine the intent of the input with asatisfactory level of confidence. However, it will also be apparent thatin certain instances the input device 210 may be unable to make contactwith the central device 220 for various reasons (e.g., a networkconnection may be unavailable, or a processing bottleneck at the centraldevice 220 may cause communication delays). In such cases, the inputdevice 210 that has initiated contact with the central device 220 mayshift into decentralized processing (e.g., as described in reference toFIG. 6) and communicate capabilities with one or more of the otherdevices 210 a-n in the constellation model. Thus, when the centraldevice 220 cannot be invoked for various reasons, the remaining devices210 a-n may operate as cooperative nodes to determine intent in adecentralized manner.

Additionally, in the multi-device voice services environment, thecentral device 220 and the various other devices 210 a-n may cooperateto create a converged model of capabilities throughout the environment.For example, as indicated above, in addition to having intentdetermination capabilities based on processing resources, memoryresources, and device capabilities, each of the devices 210 a-n and thecentral device 220 may include various other natural language processingcomponents. The voice services environment may therefore operate in anintegrated manner by maintaining not only a complete model of data,content, and services associated with the various devices 210 a-n, butalso of other natural language processing capabilities and dynamicstates associated with the various devices 210 a-n. As such, the variousdevices 210 a-n may operate with a goal of converging capabilities,data, states, and other information across the device, either on onedevice (e.g., the central device 220) or distributed among the variousdevices 210 a-n (e.g., as in the decentralized implementation to bedescribed in FIG. 6).

For example, as discussed above, each device 210 includes an AutomaticSpeech Recognizer, one or more dynamically adaptable recognitiongrammars, and vocabulary lists used to generate phonemic interpretationsof natural language utterances. Moreover, each device 210 includeslocally established context, which can range from information containedin a context stack, context and namespace variables, vocabularytranslation mechanisms, short-term shared knowledge relating to acurrent dialogue or conversational interaction, long-term sharedknowledge relating to a user's learned preferences over time, or othercontextual information. Furthermore, each device 210 may have variousservices or applications associated therewith, and may perform variousaspects of natural language processing locally. Thus, additionalinformation to be converged throughout the environment may includepartial or preliminary utterance recognitions, misrecognitions orambiguous recognitions, inferencing capabilities, and overall devicestate information (e.g., songs playing in the environment, alarms set inthe environment, etc.).

Thus, various data synchronization and referential integrity algorithmsmay be employed in concert by the various devices 210 a-n and thecentral device 220 to provide a consistent worldview of the environment.For example, information may be described and transmitted throughout theenvironment for synchronization and convergence purposes using theUniversal Plug and Play protocol designed for computer ancillarydevices, although the environment can also operate in a peer-to-peerdisconnected mode (e.g., when the central device 220 cannot be reached).However, in various implementations, the environment may also operate ina peer-to-peer mode regardless of the disconnected status, asillustrated in FIG. 6, for example, when the devices 210 a-n havesufficient commensurate resources and capabilities for natural languageprocessing.

In general, the algorithms for convergence in the environment can beexecuted at various intervals, although it may be desirable to limitdata transmission so as to avoid processing bottlenecks. For example,because the convergence and synchronization techniques relate to naturallanguage processing, in which any given utterance will typically beexpressed over a course of several seconds, information relating tocontext and vocabulary need not be updated on a time frame of less thana few seconds. However, as communication capabilities permit, contextand vocabulary could be updated more frequently to provide real-timerecognition or the appearance of real-time recognition. In anotherimplementation, the convergence and synchronization may be permitted torun until completion (e.g., when no requests are currently pending), orthe convergence and synchronization may be suspended or terminated whena predetermined time or resource consumption limit has been reached(e.g., when the convergence relates to a pending request, an intentdetermination having a highest confidence level at the time of cut-offmay be used).

By establishing a consistent view of capabilities, data, states, andother information throughout the environment, an input device 210 maycooperate with the central device 220 and one or more secondary devices(i.e., one or more of devices 210 a-n, other than the input device) inprocessing any given multi-modal natural language input. Furthermore, byproviding each device 210 and the central device 220 with aconstellation model that describes a synchronized state of theenvironment, the environment may be tolerant of failure by one or moreof the devices 210 a-n, or of the central device 220. For example, ifthe input device 210 cannot communicate with the central device 220(e.g., because of a server crash), the input device 210 may enter adisconnected peer-to-peer mode, whereby capabilities can be exchangedwith one or more devices 210 a-n with which communications remainavailable. To that end, when a device 210 establishes new informationrelating to vocabulary, context, misrecognitions, agent adaptation,intent determination capabilities, inferencing capabilities, orotherwise, the device 210 may transmit the information to the centraldevice 220 for convergence purposes, as discussed above, in addition toconsulting the constellation model to determine whether the informationshould be transmitted to one or more of the other devices 210 a-n.

For example, suppose the environment includes a voice-enabled mobilephone that has nominal functionality relating to playing music or othermedia, and which further has a limited amount of local storage space,while the environment further includes a voice-enabled home media systemthat includes a mass storage medium that provides dedicated mediafunctionality. If the mobile phone were to establish new vocabulary,context, or other information relating to a song (e.g., a user downloadsthe song or a ringtone to the mobile phone while on the road), themobile phone may transmit the newly established information to the homemedia system in addition to the central device 220. As such, by having amodel of all of the devices 210 a-n in the environment and transmittingnew information to the devices where it will most likely be useful, thevarious devices may handle disconnected modes of operation when thecentral device 220 may be unavailable for any reason, while resourcesmay be allocated efficiently throughout the environment.

Thus, based on the foregoing discussion, it will be apparent that acentralized implementation of an integrated multi-device voice servicesenvironment may generally include a central device 220 operable toaggregate or converge knowledge relating to content, services,capabilities, and other information associated with variousvoice-enabled devices 210 a-n deployed within the environment. In suchcentralized implementations, the central device 220 may be invoked as arecognizer of first or last resort, as will be described in greaterdetail with reference to FIGS. 3-5, and furthermore, the other devices210 a-n in the environment may be configured to automatically enter adisconnected or peer-to-peer mode of operation when the central device220 cannot be invoked for any reason (i.e., devices may enter adecentralized or distributed mode, as will be described in greaterdetail with reference to FIGS. 6-7). Knowledge and capabilities of eachof the devices 210 a-n may therefore be made available throughout thevoice services environment in a centralized manner, a distributedmanner, or various combinations thereof, thus optimizing an amount ofnatural language processing resources used to determine an intent of anygiven multi-modal natural language input.

According to various aspects of the invention, FIG. 3 illustrates a flowdiagram of an exemplary method for processing multi-modal, naturallanguage inputs at an input device in the centralized implementation ofthe integrated, multi-modal, multi-device natural language voice serviceenvironment. Similarly, FIGS. 4 and 5 illustrate corresponding methodsassociated with a central device and one or more secondary devices,respectively, in the centralized voice service environment. Furthermore,it will be apparent that the processing techniques described in relationto FIGS. 3-5 may generally be based on the centralized implementationillustrated in FIG. 2 and described above, whereby the input device maybe assumed to be distinct from the central device, and the one or moresecondary devices may be assumed to be distinct from the central deviceand the input device. However, it will be apparent that variousinstances may involve a natural language input being received at thecentral device or at another device, in which case the techniquesdescribed in FIGS. 3-5 may be vary depending on circumstances of theenvironment (e.g., decisions relating to routing utterances to aspecific device or devices may be made locally, collaboratively, or inother ways depending on various factors, such as overall system state,communication capabilities, intent determination capabilities, orotherwise).

As illustrated in FIG. 3, a multi-modal natural language input may bereceived at an input device in an operation 310. The multi-modal inputmay include at least a natural language utterance provided by a user,and may further include other input modalities such as audio, text,button presses, gestures, or other non-voice inputs. It will also beapparent that prior to receiving the natural language input in operation310, the input device may be configured to establish natural languageprocessing capabilities. For example, establishing natural languageprocessing capabilities may include, among other things, loading anAutomatic Speech Recognizer and any associated recognition grammars,launching a conversational language processor to handle dialogues withthe user, and installing one or more domain agents that providefunctionality for respective application domains or contextual domains(e.g., navigation, music, movies, weather, information retrieval, devicecontrol, etc.).

The input device may also be configured to coordinate synchronization ofintent determination capabilities, shared knowledge, and otherinformation with the central device and the secondary devices in theenvironment prior to receiving the input at operation 310. For example,when the input device installs a domain agent, the installed domainagent may bootstrap context variables, semantics, namespace variables,criteria values, and other context related to that agent from otherdevices in the system. Similarly, misrecognitions may be received fromthe central device and the secondary devices in order to enablecorrection of agents that use information relevant to the receivedmisrecognitions, and vocabularies and associated translation mechanismsmay be synchronized among the devices to account for potentialvariations between the Automatic Speech Recognizers used by the variousdevices (e.g., each device in the environment cannot be guaranteed touse the same Automatic Speech Recognizer or recognition grammars,necessitating vocabulary and translation mechanisms to be shared amongthe devices that share intent determination capabilities).

Upon establishing and synchronizing natural language processingcapabilities and subsequently receiving a multi-modal natural languageinput in operation 310, the input device may determine whether theenvironment has been set up to automatically transmit the input to thecentral device in a decisional operation 320. In such a case, processingproceeds to an operation 360 for transmitting the input to the centraldevice, which may then process the input according to techniques to bedescribed in relation to FIG. 4. If the environment has not been set upto automatically communicate the input to the central device, however,processing proceeds to an operation 330, where the input device performstranscription of the natural language utterance contained in themulti-modal input. For example, the input device may transcribe theutterance using the Automatic Speech Recognizer and recognition grammarsassociated therewith according to techniques described above and in theabove-referenced U.S. patent applications.

Subsequently, in an operation 340, an intent of the multi-modal naturallanguage input may be determined at the input device using local naturallanguage processing capabilities and resources. For example, anynon-voice input modalities included in the input may be merged with theutterance transcription and a conversational language processorassociated with the input device may utilize local information relatingto context, domain knowledge, shared knowledge, context variables,criteria values, or other information useful in natural languageprocessing. As such, the input device may attempt to determine a bestguess as to an intent of the user that provided the input, such asidentifying a conversation type (e.g., query, didactic, or exploratory)or request that may be contained in the input (e.g., a command or queryrelating to one or more domain agents or application domains).

The intent determination of the input device may be assigned aconfidence level (e.g., a device having an Automatic Speech Recognizerthat implements multi-pass analysis may assign comparatively higherconfidence levels to utterance transcriptions created thereby, which mayresult in a higher confidence level for the intent determination). Theconfidence level may be assigned based on various factors, as describedin the above-referenced U.S. patent applications. As such, a decisionaloperation 350 may include determining whether the intent determinationof the input device meets an acceptable level of confidence. When theintent determination meets the acceptable level confidence, processingmay proceed directly to an operation 380 where action may be taken inresponse thereto. For example, when the intent determination indicatesthat the user has requested certain information, one or more queries maybe formulated to retrieve the information from appropriate informationsources, which may include one or more of the other devices. In anotherexample, when the intent determination indicates that the user hasrequested a given command (e.g., to control a specific device), thecommand may be routed to the appropriate device for execution.

Thus, in cases where the input device can determine the intent of anatural language input without assistance from the central device or thesecondary devices, communications and processing resources may beconserved by taking immediate action as may be appropriate. On the otherhand, when the intent determination of the input device does not meetthe acceptable level of confidence, decisional operation 350 may resultin the input device requesting assistance from the central device inoperation 360. In such a case, the multi-modal natural language inputmay be communicated to the central device in its entirety, whereby thecentral device processes the input according to techniques described inFIG. 4. However, should transmission to the central device fail for somereason, the input device may shift into a disconnected peer-to-peer modewhere one or more secondary devices may be utilized, as will bedescribed below in relation to FIG. 7. When transmission to the centraldevice occurs without incident, however, the input device may receive anintent determination from the central device in an operation 370, andmay further receive results of one or more requests that the centraldevice was able to resolve, or requests that the central device hasformulated for further processing on the input device. As such, theinput device may take action in operation 380 based on the informationreceived from the central device in operation 370. For example, theinput device may route queries or commands to local or remoteinformation sources or devices based on the intent determination, or maypresent results of the requests processed by the central device to theuser.

Referring to FIG. 4, the central device may receive the multi-modalnatural language input from the input device in an operation 410. Thecentral device, having aggregated context and other knowledge fromthroughout the environment, may thus transcribe the utterance in anoperation 420 and determine an intent of the input from the transcribedutterance in an operation 430. As such, the central device may considerinformation relating to context, domain agents, applications, and devicecapabilities throughout the environment in determining the intent of theutterance, including identification of one or more domains relevant tothe input. However, it will be apparent that utilizing informationaggregated from throughout the environment may cause ambiguity oruncertainty in various instances (e.g., an utterance containing the word“traffic” may have a different intent in domains relating to movies,music, and navigation).

As such, once the central device has attempted to determine the intentof the natural language input, a determination may be made in anoperation 440 as to whether one or more secondary devices (i.e., otherdevices in the constellation besides the input device) may also becapable of intent determination in the identified domain or domains.When no such secondary devices can be identified, decisional operation440 may branch directly to an operation 480 to send to the input devicethe determined intent and any commands, queries, or other requestsidentified from the determined intent.

On the other hand, when one or more secondary devices in the environmenthave intent determination capabilities in the identified domain ordomains, the natural language input may be sent to such secondarydevices in an operation 450. The secondary devices may then determine anintent as illustrated in FIG. 5, which may include techniques generallysimilar to those described above in relation to the input device andcentral device (i.e., the natural language input may be received in anoperation 510, an utterance contained therein may be transcribed in anoperation 520, and an intent determination made in an operation 530 maybe returned to the central device in an operation 540).

Returning to FIG. 4, the central device may collate intent determinationresponses received from the secondary devices in an operation 460. Forexample, as indicated above, the central device may identify one or moresecondary devices capable of determining intent in a domain that thecentral device has identified as being relevant to the natural languageutterance. As will be apparent, the secondary devices invoked inoperation 450 may often include a plurality of devices, and intentdetermination responses may be received from the secondary devices in aninterleaved manner, depending on processing resources, communicationsthroughput, or other factors (e.g., the secondary devices may include atelematics device having a large amount of processing power and abroadband network connection and an embedded mobile phone having lessprocessing power and only a cellular connection, in which case thetelematics device may be highly likely to provide results to the centraldevice before the embedded mobile phone). Thus, based on potentialvariations in response time of secondary devices, the central device maybe configured to place constraints on collating operation 460. Forexample, the collating operation 460 may be terminated as soon as anintent determination has been received from one of the secondary devicesthat meets an acceptable level of confidence, or the operation 460 maybe cut off when a predetermined amount of time has lapsed or apredetermined amount of resources have been consumed. In otherimplementations, however, it will be apparent that collating operation460 may be configured to run to completion, regardless of whether delayshave occurred or suitable intent determinations have been received.Further, it will be apparent that various criteria may be used todetermine whether or when to end the collating operation 460, includingthe nature of a given natural language input, dialogue, or otherinteraction, or system or user preferences, among other criteria, aswill be apparent.

In any event, when the collating operation 460 has completed, asubsequent operation 470 may include the central device arbitratingamong the intent determination responses received from one or more ofthe secondary devices previously invoked in operation 450. For example,each of the invoked secondary devices that generate an intentdetermination may also assign a confidence level to that intentdetermination, and the central device may consider the confidence levelsin arbitrating among the responses. Moreover, the central device mayassociate other criteria with the secondary devices or the intentdeterminations received from the secondary devices to further enhance alikelihood that the best intent determination will be used. For example,various ones of the secondary devices may only be invoked for partialrecognition in distinct domains, and the central device may aggregateand arbitrate the partial recognitions to create a completetranscription. In another example, a plurality of secondary devices maybe invoked to perform overlapping intent determination, and the centraldevice may consider capabilities of the secondary devices to weigh therespective confidence levels (e.g., when one of two otherwise identicalsecondary devices employs multi-pass speech recognition analysis, thesecondary device employing the multi-pass speech recognition analysismay be weighed as having a higher likelihood of success). It will beapparent that the central device may be configured to arbitrate andselect one intent determination from among all of the intent hypotheses,which may include the intent determination hypothesis generated by thecentral device in operation 430. Upon selecting the best intentdetermination hypothesis, the central device may then provide thatintent determination to the input device in operation 480, as well asany commands, queries, or other requests that may be relevant thereto.The input device may then take appropriate action as described above inrelation to FIG. 3.

According to various aspects of the invention, FIG. 6 illustrates ablock diagram of an exemplary distributed implementation of theintegrated, multi-modal, multi-device natural language voice serviceenvironment. As described above, the distributed implementation may alsobe categorized as a disconnected or peer-to-peer mode that may beemployed when a central device in a centralized implementation cannot bereached or otherwise does not meet the needs of the environment. Thedistributed implementation illustrated in FIG. 6 may be generallyoperate with similar purposes as described above in relation to thecentralized implementation (i.e., to ensure that the environmentincludes a comprehensive model of aggregate knowledge and capabilitiesof a plurality of devices 610 a-n in the environment). Nonetheless, thedistributed implementation may operate in a somewhat different manner,in that one or more of the devices 610 a-n may be provided with theentire constellation model, or various aspects of the model may bedistributed among the plurality of devices 610 a-n, or variouscombinations thereof.

Generally speaking, the plurality of voice-enabled devices 610 a- may becoupled to one another by a voice services interface 630, which mayinclude any suitable real or virtual interface (e.g., a common messagebus or network interface, a service-oriented abstraction layer, etc.).The various devices 610 a-n may therefore operate as cooperative nodesin determining intent for multi-modal natural language utterancesreceived by any one of the devices 610. Furthermore, the devices 610 a-nmay share knowledge of vocabularies, context, capabilities, and otherinformation, while certain forms of data may be synchronized to ensureconsistent processing among the devices 610 a-n. For example, becausenatural language processing components used in the devices 610 a-n mayvary (e.g., different recognition grammars or speech recognitiontechniques may exist), vocabulary translation mechanisms,misrecognitions, context variables, criteria values, criteria handlers,and other such information used in the intent determination processshould be synchronized to the extent that communication capabilitiespermit.

By sharing intent determination capabilities, device capabilities,inferencing capabilities, domain knowledge, and other information,decisions as to routing an utterance to a specific one of the devices610 a-n may be made locally (e.g., at an input device), collaboratively(e.g., a device having particular capabilities relevant to the utterancemay communicate a request to process the utterance), or variouscombinations thereof (e.g., the input device may consider routing tosecondary devices only when an intent of the utterance cannot bedetermined). Similarly, partial recognition performed at one or more ofthe devices 610 a-n may be used to determine routing strategies forfurther intent determination of the utterance. For example, an utterancethat contains a plurality of requests relating to a plurality ofdifferent domains may be received at an input device that can onlydetermine intent in one of the domains. In this example, the inputdevice may perform partial recognition for the domain associated withthe input device, and the partial recognition may also identify theother domains relevant to the utterance for which the input device doesnot have sufficient recognition information. Thus, the partialrecognition performed by the input device may result in identificationof other potentially relevant domains and a strategy may be formulatedto route the utterance to other devices in the environment that includerecognition information for those domains.

As a result, multi-modal natural language inputs, including naturallanguage utterances, may be routed among the various devices 610 a-n inorder to perform intent determination in a distributed manner. However,as the capabilities and knowledge held by any one of the devices 610 a-nmay vary, each of the devices 610 a-n may be associated with areliability factor for intent determinations generated by the respectivedevices 610 a-n. As such, to ensure that final intent determinations canbe relied upon with a sufficient level of confidence, knowledge may bedistributed among the devices 610 a-n to ensure that reliability metricsfor intent determinations provided by each of the devices 610 a-n arecommensurable throughout the environment. For example, additionalknowledge may be provided to a device having a low intent determinationreliability, even when such knowledge results in redundancy in theenvironment, to ensure commensurate reliability of intent determinationenvironment-wide.

Therefore, in distributed implementations of the integrated voiceservices environment, utterances may be processed in various ways, whichmay depend on circumstances at a given time (e.g., system states, systemor user preferences, etc.). For example, an utterance may be processedlocally at an input device and only routed to secondary devices when anintent determination confidence level falls below a given threshold. Inanother example, utterances may be routed to a specific device based onthe modeling of knowledge and capabilities discussed above. In yetanother example, utterances may be flooded among all of the devices inthe environment, and arbitration may occur whereby intent determinationsmay be collated and arbitrated to determine a best guess at intentdetermination.

Thus, utterances may be processed in various ways, including throughlocal techniques, centralized techniques, distributed techniques, andvarious combinations thereof. Although many variations will be apparent,FIG. 7 illustrates an exemplary method for combined local anddistributed processing of multi-modal, natural language inputs in adistributed implementation of the voice service environment, accordingto various aspects of the invention. In particular, the distributedprocessing may begin in an operation 710, where a multi-modal naturallanguage input may be received at an input device. The input device maythen utilize various natural language processing capabilities associatedtherewith in an operation 720 to transcribe an utterance contained inthe multi-modal input (e.g., using an Automatic Speech Recognizer andassociated recognition grammars), and may subsequently determine apreliminary intent of the multi-modal natural language input in anoperation 730. It will be apparent that operations 710 through 730 maygenerally be performed using local intent determination capabilitiesassociated with the input device.

Thereafter, the input device may invoke intent determinationcapabilities of one or more secondary devices in an operation 740. Moreparticularly, the input device may provide information associated withthe multi-modal natural language input to one or more of the secondarydevices, which may utilize local intent determination capabilities toattempt to determine intent of the input using techniques as describedin relation to FIG. 5. It will also be apparent that, in variousimplementations, the secondary devices invoked in operation 740 mayinclude only devices having intent determination capabilities associatedwith a specific domain identified as being associated with the input. Inany event, the input device may receive intent determinations from theinvoked secondary devices in an operation 750, and the input device maythen collate the intent determinations received from the secondarydevices. The input device may then arbitrate among the various intentdeterminations, or may combine various ones of the intent determinations(e.g., when distinct secondary devices determine intent in distinctdomains), or otherwise arbitrate among the intent determinations todetermine a best guess at the intent of the multi-modal natural languageinput (e.g., based on confidence levels associated with the variousintent determinations). Based on the determined intent, the input devicemay then take appropriate action in an operation 770, such as issuingone or more commands, queries, or other requests to be executed at oneor more of the input device or the secondary devices.

Furthermore, in addition to the exemplary implementations describedabove, various implementations may include a continuous listening modeof operation where a plurality of devices may continuously listen formulti-modal voice-based inputs. In the continuous listening mode, eachof the devices in the environment may be triggered to accept amulti-modal input when one or more predetermined events occur. Forexample, the devices may each be associated with one or more attentionwords, such as “Phone, <multi-modal request>” for a mobile phone, or“Computer, <multi-modal request>” for a personal computer. When one ormore of the devices in the environment recognize the associatedattention word, keyword activation may result, where the associateddevices trigger to accept the subsequent multi-modal request. Further,where a plurality of devices in a constellation may be listening, theconstellation may use all available inputs to increase recognitionrates.

Moreover, it will be apparent that the continuous listening mode may beapplied in centralized voice service environments, distributedcentralized voice service environments, or various combinations thereof.For example, when each device in the constellation has a differentattention word, any given device that recognizes an attention word mayconsult a constellation model to determine a target device orfunctionality associated with the attention word. In another example,when a plurality of devices in the constellation share one or moreattention words, the plurality of devices may coordinate with oneanother to synchronize information for processing the multi-modal input,such as a start time for an utterance contained therein.

Implementations of the invention may be made in hardware, firmware,software, or various combinations thereof. The invention may also beimplemented as instructions stored on a machine-readable medium, whichmay be read and executed by one or more processors. A machine-readablemedium may include various mechanisms for storing or transmittinginformation in a form readable by a machine (e.g., a computing device).For example, a machine-readable storage medium may include read onlymemory, random access memory, magnetic disk storage media, opticalstorage media, flash memory devices, and others, and a machine-readabletransmission media may include forms of propagated signals, such ascarrier waves, infrared signals, digital signals, and others. Further,firmware, software, routines, or instructions may be described in theabove disclosure in terms of specific exemplary aspects andimplementations of the invention, and performing certain actions.However, it will be apparent that such descriptions are merely forconvenience and that such actions in fact result from computing devices,processors, controllers, or other devices executing the firmware,software, routines, or instructions.

Aspects and implementations may be described as including a particularfeature, structure, or characteristic, but every aspect orimplementation may not necessarily include the particular feature,structure, or characteristic. Further, when a particular feature,structure, or characteristic has been described in connection with anaspect or implementation, it will be understood that such feature,structure, or characteristic may be included in connection with otheraspects or implementations, whether or not explicitly described. Thus,various changes and modifications may be made to the precedingdescription without departing from the scope or spirit of the invention,and the specification and drawings should therefore be regarded asexemplary only, and the scope of the invention determined solely by theappended claims.

1-27. (canceled)
 28. A method of processing natural language utterances, the method being implemented by a first device that comprises one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, by the first device, a natural language utterance spoken by a user; performing, by the first device, speech recognition to determine one or more words of the natural language utterance; determining, by the first device, based on the one or more words, a first prediction of an intent of the user; transmitting, by the first device, the natural language utterance to a second device; receiving, from the second device by the first device, a second prediction of the intent of the user; and determining, by the first device, the intent of the user based on the first prediction and the second prediction.
 29. The method of claim 28, further comprising: determining, by the first device, processing capabilities associated with a plurality of devices, wherein the plurality of devices comprises the second device; and selecting, by the first device, based on the processing capabilities associated with the second device, the second device to determine the second prediction, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on the selection.
 30. The method of claim 29, wherein the processing capabilities associated with the plurality of devices comprise processing power, storage resources, information, or services available at individual ones of the plurality of devices.
 31. The method of claim 29, further comprising: determining, by the first device, based on the one or more words, a domain relating to the natural language utterance, wherein selecting the second device comprises selecting the second device based on the domain.
 32. The method of claim 31, wherein the plurality of devices are associated with different domains, the second device is associated with the domain, and the different domains comprise the domain.
 33. The method of claim 28, further comprising: determining, by the first device, whether a prediction of the intent of the user is to be obtained from at least one other device, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on a determination that a prediction of the intent of the user is to be obtained from at least one other device.
 34. The method of claim 33, further comprising: determining, by the first device, a confidence level associated with the first prediction; and determining, by the first device, whether the confident level satisfies a threshold level of confidence relating to intent prediction accuracy, wherein the determination that a prediction of the intent of the user is to be obtained from at least one other device is based on a determination that the confidence level does not satisfy the threshold level of confidence.
 35. The method of claim 28, further comprising: transmitting, by the first device, the natural language utterance to a third device; receiving, from the third device by the first device, a third prediction of the intent of the user; receiving, from the second device by the first device, information regarding a second confidence level associated with the second prediction; receiving, from the third device by the first device, information regarding a third confidence level associated with the third prediction; comparing, by the first device, the second and third confidence levels with one another, wherein determining the intent of the user comprises determining the intent based on the comparison.
 36. The method of claim 35, further comprising: determining, by the first device, processing capabilities associated with the second device and processing capabilities associated with the third device, wherein determining the intent comprises determining the intent based on the comparison and the processing capabilities associated with the second and third devices.
 37. The method of claim 28, further comprising: determining, by the first device, a first confidence level associated with the first prediction; receiving, from the second device by the first device, information regarding a second confidence level associated with the second prediction; determining, by the first device, a highest confidence level among the first confidence level, the second confidence level, and one or more other confidence levels associated with one or more other predictions of the intent of the user, wherein the one or more other predictions are received from one or more other devices; wherein determining the intent comprises selecting, from the first prediction, the second prediction, and the one or more other predictions, a prediction of the intent of the user that is associated with the highest confidence level.
 38. The method of claim 28, wherein the first device is designated as a central device.
 39. The method of claim 28, wherein the first device comprises an input device at which the natural language utterance is initially received from the user.
 40. The method of claim 39, further comprising: determining, by the first device, whether a third device is available to manage determination of the intent of the user, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on a determination that the third device is not available to manage determination of the intent of the user.
 41. The method of claim 40, further comprising: transmitting, by the first device, a request to a plurality of devices for information regarding processing capabilities associated with the plurality of devices based on a determination that the third device is not available to manage determination of the intent, wherein the plurality of devices comprise the second device; receiving, by the first device, information regarding the processing capabilities; and selecting, by the first device based on the processing capabilities, the second device to determine the second prediction, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on the selection.
 42. A system for processing natural language utterances, the system comprising: a first device having one or more physical processors programmed to execute one or more computer program instructions which, when executed, cause the one or more physical processors to: receive a natural language utterance spoken by a user; perform speech recognition to determine one or more words of the natural language utterance; determine, based on the one or more words, a first prediction of an intent of the user; transmit the natural language utterance to a second device; receive, from the second device, a second prediction of the intent of the user; and determine the intent of the user based on the first prediction and the second prediction.
 43. The system of claim 42, wherein the one or more physical processors are further caused to: determine processing capabilities associated with a plurality of devices, wherein the plurality of devices comprises the second device; and select, based on the processing capabilities associated with the second device, the second device to determine the second prediction, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on the selection.
 44. The system of claim 43, wherein the processing capabilities associated with the plurality of devices comprise processing power, storage resources, information, or services available at individual ones of the plurality of devices.
 45. The system of claim 43, wherein the one or more physical processors are further caused to: determine, based on the one or more words, a domain relating to the natural language utterance, wherein selecting the second device comprises selecting the second device based on the domain.
 46. The system of claim 45, wherein the plurality of devices are associated with different domains, the second device is associated with the domain, and the different domains comprise the domain.
 47. The system of claim 42, wherein the one or more physical processors are further caused to: determine whether a prediction of the intent of the user is to be obtained from at least one other device, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on a determination that a prediction of the intent of the user is to be obtained from at least one other device.
 48. The system of claim 47, wherein the one or more physical processors are further caused to: determine a confidence level associated with the first prediction; and determine whether the confident level satisfies a threshold level of confidence relating to intent prediction accuracy, wherein the determination that a prediction of the intent of the user is to be obtained from at least one other device is based on a determination that the confidence level does not satisfy the threshold level of confidence.
 49. The system of claim 42, wherein the one or more physical processors are further caused to: transmit the natural language utterance to a third device; receive, from the third device, a third prediction of the intent of the user; receive, from the second device, information regarding a second confidence level associated with the second prediction; receive, from the third device, information regarding a third confidence level associated with the third prediction; compare, by the first device, the second and third confidence levels with one another, wherein determining the intent of the user comprises determining the intent based on the comparison.
 50. The system of claim 49, wherein the one or more physical processors are further caused to: determine processing capabilities associated with the second device and processing capabilities associated with the third device, wherein determining the intent comprises determining the intent based on the comparison and the processing capabilities associated with the second and third devices.
 51. The system of claim 42, wherein the one or more physical processors are further caused to: determine a first confidence level associated with the first prediction; receive, from the second device, information regarding a second confidence level associated with the second prediction; determine a highest confidence level among the first confidence level, the second confidence level, and one or more other confidence levels associated with one or more other predictions of the intent of the user, wherein the one or more other predictions are received from one or more other devices; wherein determining the intent comprises selecting, from the first prediction, the second prediction, and the one or more other predictions, a prediction of the intent of the user that is associated with the highest confidence level.
 52. The system of claim 42, wherein the first device is designated as a central device.
 53. The system of claim 42, wherein the first device comprises an input device at which the natural language utterance is initially received from the user.
 54. The system of claim 53, wherein the one or more physical processors are further caused to: determine whether a third device is available to manage determination of the intent of the user; transmit a request to a plurality of devices for information regarding processing capabilities associated with the plurality of devices based on a determination that the third device is not available to manage determination of the intent, wherein the plurality of devices comprise the second device; receive information regarding the processing capabilities; and select, based on the processing capabilities, the second device to determine the second prediction, wherein transmitting the natural language utterance comprises transmitting the natural language utterance to the second device based on the selection. 